Some wise words from Georgia Tech’s Peter Webster on our ability to predict the future incidence of tropical cyclones (or TCs, which includes hurricanes):

Unless we can explain physically the history of the number and intensity of TCs in the recent past, then determining the number and intensity of TCs in the future will be either an extrapolation of very poor data sets or a belief in incomplete and inexact models.

Lucia Liljegren has a considered post up on Real Climate’s odd post on my recent letter to Nature Geoscience. I apologize for our comment problems on that thread, but perhaps this one will work better, and you can always comment at Lucia’s site, or try to get through the screeners at Real Climate. Is it just me or has the Real Climate discussion board become completely empty of anything resembling scientific discussion?

The folks at the Real Climate blog have offered up some comments on my letter to Nature Geosciences (PDF) which appeared last week. In the condescending tone that we have come to expect from Real Climate, they helpfully frame their comments in terms of teaching me some lessons. I encourage you to read the whole post, but here is my response (submitted for their posting approval) to their three main points, which I’ve highlighted in bold:

Thanks for this discussion. Full text of the letter can be found here:

http://sciencepolicy.colorado.edu/admin/publication_files/resource-2592-2008.07.pdf

1. IPCC already showed a very similar comparison as Pielke does, but including uncertainty ranges.

RESPONSE: Indeed, and including the uncertainty ranges would not change my conclusion that:

“Temperature observations fall at
the low end of the 1990 IPCC forecast range
and the high end of the 2001 range. Similarly,
the 1990 best estimate sea level rise projection
overstated the resulting increase, whereas the
2001 projection understated that rise.”

2. If a model-data comparison is done, it has to account for the uncertainty ranges – both in the data (that was Lesson 1 re noisy data) and in the model (that’s Lesson 2).

RESPONSE: I did not do a “model-data comparison”. One should be done, though, I agree.

3. One should not mix up a scenario with a forecast – I cannot easily compare a scenario for the effects of greenhouse gases alone with observed data, because I cannot easily isolate the effect of the greenhouse gases in these data, given that other forcings are also at play in the real world.

RESPONSE: Indeed. However, I made no claims about attribution, so this is not really relevant to my letter.

Thanks again, and I’ll be happy to follow the discussion.

Nature’s Climate Feedback blog provides a nice summary of a correspondence that I authored published today in Nature Geoscience:

Today in a letter to Nature Geoscience (subscription required), Roger Pielke, Jr, questions whether models from that 2001 generation improve on the predictive power of their forbears.

Pielke checks predictions from all four IPCC reports, dating back to 1990, against reality. Each report made a series of ‘if-then’ statements about the likely results of various emissions scenarios; in hindsight, Pielke can pick out which of these possible greenhouse experiments has actually been running on Earth since 1990 and compare the results to the IPCC’s shifting hypotheses.

Whereas the 2001 projections undershot the observed temperatures and sea levels, the 1990 projections overshot them, he concludes. Projections of temperature and sea level fell substantially between the 1990 and 1995 IPCC reports, when aerosols were added to models and carbon-cycle simulations were tweaked. But because they dropped too far, the adjusted post-1995 projections “are not obviously superior in capturing climate evolution”, says Pielke.

Now according to Grist Magazine’s Joe Romm I am a “delayer/denier” because I’ve asked what data would be inconsistent with IPCC predictions. Revealed truths are not to be questioned lest we take you to the gallows. And people wonder why some people see the more enthusiastic climate advocates akin to religious zealots.

I am happy to report that it is quite possible to believe in strong action on mitigation and adaptation while at the same time ask probing questions of our scientific understandings.

UPDATE 2:40PM 3-15-08: Within a few hours of this post, as we might have expected, rather than contributing to the substantive discussion, a climate scientist chooses instead to tell us how stupid we are for even discussing such subjects. We are told that “until the temperature obviously and unambiguously turns up again, this kind of stuff is going to continue.” Isn’t that what this post says? For the “stuff” read on below.

Regular readers will recall that not long ago I asked the climate community research community to suggest what climate observations might be observed on decadal time scales that might be inconsistent with predictions from models. While Real Climate has decided to take a pass on this question other scientists and interested observers have taken up the challenge, no doubt with interest added by the recent cooling in the primary datasets of global temperature.

A very interesting perspective is provided by Lucia Liljegren, who has several interesting posts on observations versus predictions. The figure above is from her analysis. Her complete analysis can be found here. She has several follow up posts in which she discusses other aspects of the analysis and links to a few other, similar explorations of this issue. She writes:

No matter which major temperature measuring group we examine, or which reasonable criteria for limiting our choices we select, it appears that possible that something not anticipated by the IPCC WG1 happened soon after they published their predictions for this century. That something may be the shift in the Pacific Decadal Oscillation; it may be something else. Statistics cannot tell us.

It may turn out that this something is a relatively infrequent but climatologically important, feature that results in unusually cold weather . Events that happen at a rate of 1% do happen– at a rate of 1%. So, if recent flat trend is the 1% event, then 30 year trend in temperatures will resume.

For what it’s worth: I believe AGW is real, based on physical arguments and longer term trends, I suspect we will discover that GCM’s are currently unable to predict shifts in the PDO. The result is the uncertainty intervals on IPCC projections for the short term trend were much too small.

Of course, the reason for the poor short term predictions may turn out to be something else entirely. It remains to those who make these predictions to try to identify what, if anything, resulted in this mismatch between projections and short term data. Or to stand steadfast and wait for La Nina to break and the weather to begin to warm.

Those wanting to quibble with her analysis would no doubt observe that the uncertainty around IPCC predictions for the short term is undoubtedly larger that then IPCC itself presented. Lucia in fact suggests this in her analysis, making one wonder if uncertainties are indeed larger than presented, why didn’t the IPCC say so?

In 2006 my father and I wrote about the possible effects on the climate debate of short-term predictions that do not square with observations:

predictions represent a huge gamble with public and policymaker opinion. If more-or-less steady global warming does not occur as forecast by these models, not only will professional reputations be at risk, but the need to reduce threats to the wide spectrum of serious and legitimate environmental concerns (including the human release of greenhouse gases) will be questioned by some as having been oversold. For better or worse, a failure to accurately predict the changes in the global average surface temperature, global average tropospheric temperature, ocean average heat content change, or Arctic sea ice coverage would raise questions on the reliance of global climate models for accurate prediction on multi-decadal time scales.

In one of the comments in response to that post a climate scientist (and Real Climate blogger) took us to task for raising the issue suggesting that there was no really reason to speculate about such things given that, “I’ve pointed out that in the obs, there is no sign of > 2 yr decreasing trends.”

Another climate scientist commented that climate models were completely on target:

Re the possibility that the Earth is acting in a way that the models hadn’t predicted, I must say I’m pretty relaxed about that. Let’s wait a few more years and see, eh?

I have not yet seen rebuttals to Lucia’s analysis, or others like it (she points to a few), which are not peer-reviewed analyses, yet certainly of some merit and worth considering. There continues to be good reasons for climate scientists to begin more openly discussing the limitations of short-term climate predictions and the implications for understanding uncertainties. They have these discussions among themselves all of the time. For example, with a view quite similar to my own, Real Climate’s Gavin Schimdt suggests that if the full context of a prediction from a climate model is not understood, then:

model results have an aura of exactitude that can be misleading. Reporting those results without the appropriate caveats can then provoke a backlash from those who know better, lending the whole field an aura of unreliability.

None of this discussion means that the basic conclusion that greenhouse gases affect the climate system is wrong, or that action to mitigate emissions do not make sense. What it does mean is that we should be concerned about the overselling of climate predictions and the corresponding risks to public credibility and advocacy built upon these predictions.

At a recent conference on adaptation in London, I co-authored a presented paper (with Suraje Dessai, Mike Hulme, and Rob Lempert) on the the role of climate model forecasts in support of adaptation. Our argument is that climate models don’t forecast very well on time and spatial scales of relevance to decision makers facing adaptation choices, and even if they did, given irreducible uncertainties robust decision making is a better approach than seeking to optimize.

For more evidence of why it is that climate models are of little predictive use in adaptation decision making, consider the recent discussion of cooling in Antarctica and the southern oceans from RealClimate:

The pioneer climate modelers Kirk Bryan and Syukuro Manabe took up the question with a more detailed model that revealed an additional effect. In the Southern Ocean around Antarctica the mixing of water went deeper than in Northern waters, so more volumes of water were brought into play earlier. In their model, around Antarctica “there is no warming at the sea surface, and even a slight cooling over the 50-year duration of the experiment.” In the twenty years since, computer models have improved by orders of magnitude, but they continue to show that Antarctica cannot be expected to warm up very significantly until long after the rest of the world’s climate is radically changed.

Bottom line: A cold Antarctica and Southern Ocean do not contradict our models of global warming. For a long time the models have predicted just that.

Today CSIRO in Australia reports that southern oceans have in fact been warming:

The longest continuous record of temperature changes in the Southern Ocean has found that Antarctic waters are warming and sea levels are rising, an Australian scientist said Monday.

I have no doubt that these observations of warming will also be found, somehow, to be consistent with predictions of climate models. And that is the problem; climate scientists, especially those involved in political advocacy for action on climate change, steadfastly refuse to describe what observations over the short term (i.e., when most adaptation decisions are made) would be inconsistent with model predictions. So all observations are consistent with predictions of climate models.

The reason for this situation of total ambiguity is a perceived need to maintain the public credibility of climate model predictions over the very long term in support of political action on climate change in the face of relentless attacks for politically motivated skeptics. So what do we get? Nonsensical and useless pronouncements such as a cooling southern ocean and a warming southern ocean are both consistent with climate model predictions, thus we can trust the models.

The lesson for decision makers grappling with adaptation to future climate changes? Make sure that your decisions are robust to a wide range of future possibilities, and use caution in seeking to optimize based on this or that prediction of the near-term future.

The figure above shows the snowpack in the state of Colorado for the past few years. The current level is higher than its been for a while. This is great news for just about everyone in Colorado — except seasonal climate forecasters, who had predicted a dry, warm winter, and were sticking to that forecast as recently as a month ago.

In today’s Denver Post our excellent local science reporter Katy Human takes a look at the forecasts and why forecasters have given in to the reality of massive snowfall totals here in Colorado:

Dry-winter forecasts were flat wrong this year for much of Colorado and the Southwest, and weather experts say they’re struggling to understand why the snow just keeps falling.

Some forecasters blame climate change, and others point to the simple vicissitudes of weather. Regardless, almost everyone called for a dry-to-normal winter in Colorado and the Southwest — but today, the state’s mountains are piled so thick with snow that state reservoirs could fill and floods could be widespread this spring.

“The polar jet stream has been on steroids. We don’t understand this. It’s pushing our limits, and it’s humbling,” said Klaus Wolter, a meteorologist with the National Oceanic and Atmospheric Administration and the University of Colorado at Boulder.

Wolter and NOAA both forecast a drier-than-average winter in most of Colorado. AccuWeather Inc. did the same, citing similar reasons: A La Niña weather system of cool, equatorial Pacific water had set up in the tropics last fall.

I have a lot of respect for Klaus, who is brave enough to put out forecasts in the public on time scales that will allow verification, and hence newspaper articles on his performance. Forecasting is not for the thin-skinned. But forecasts have other effects as well:

La Niña winters have almost always brought droughtlike conditions to the Southwest, as the jet stream ferries storms farther north.

But Arizona has been hit with record snowfall this winter, said Mark Hubble, a senior hydrologist with the Salt River Project in Arizona, the largest provider of water and power in Phoenix.

Dry forecasts last fall convinced Salt River Project managers to purchase about 20,000 acre-feet of water from the Central Arizona Project as a backup, Hubble said. An acre- foot of water is about enough for a family of four for one year.

“As it turns out, we didn’t need it — at all,” Hubble said. He could not estimate the financial losses to Salt River, because some payments are made in-kind, with water trades and offsets in the future.

So why did the forecasts bust this year? No one really knows.

Wolter said he’s troubled that his and other long-range forecasts have been off two years in a row now.

Last year, experts predicted a wet year from Southern California across to Arizona and southern Colorado, because of an El Niño weather system of warmer Pacific water.

Instead, drought worsened in the Southwest, capped by a huge fire season in Southern California.

“So we have two years in a row here where the atmosphere does not behave as we expect,” Wolter said. “Maybe global changes are pulling the rug out from underneath us. We may not know the answer for 10 years, . . . but one pet answer is that you should get more variability with global change.”

So I suppose we should add busted seasonal forecasts to our growing lists of things consistent with predictions of climate change. Making long term, unverifiable forecasts is sure a lot safer territory than predicting seasonal snowpack!

For further reading:

Pielke, Jr., R. A., 2000: Policy Responses to the 1997/1998 El Niño: Implications for Forecast Value and the Future of Climate Services. Chapter 7 in S. Changnon (ed.), The 1997/1998 El Niño in the United States. Oxford University Press: New York. 172-196. (PDF)

I have been intrigued by the frequent postings over at Real Climate in defense of the predictive ability of climate models. The subtext of course is political – specifically that criticisms of climate models are an unwarranted basis for criticizing climate policies that are justified or defended in terms of the results of climate models. But this defensive stance risks turning climate modeling from a scientific endeavor to a pseudo-scientific exercise in the politics of climate change.

(more…)

It is true that the calculus of environmental tradeoffs will be inevitably and irretrievably changed due to consideration of climate change. Ideas that were convenient (convenient untruths) like “the world worked fine without humans, if we remove their influence it will go back to what it should be” have continued to provide the implicit underpinning for much scientific effort. In short, people gravitated to the concept that “if we studied how things used to be” (pre- European settlement) we would know how they “should” be, with no need for discussions of values or involving non-scientists. This despite excellent work such as the book Discordant Harmonies by Dan Botkin, that displayed the scientific flaws in this reasoning (in 1992).

What’s interesting to me in the recent article, “The Preservation Predicament”, by Cornelia Dean in The New York Times
is the implicit assumption that conservationists and biologists will be the ones who determine whether investing in conservation in the Everglades compared to somewhere else, given climate change, is a good idea – perhaps implying that sciences like decision science or economics have little to contribute to the dialog. Not to speak of communities and their elected officials.

I like to quote the IUCN (The World Conservation Union) governance principles:

Indigenous and local communities are rightful primary partners in the development and implementation of conservation strategies that affect their lands, waters, and other resources, and in particular in the establishment and management of protected areas.

Is it more important for scientists to “devise theoretical frameworks for deciding when, how or whether to act” (sounds like decision science) or for folks in a given community, or interested in a given species, to talk about what they think needs to be done and why? There are implicit assumptions about what sciences are the relevant ones and the relationship between science and democracy, which in my opinion need to be debated in the light of day rather than assumed.

Sharon Friedman
Director, Strategic Planning
Rocky Mountain Region
USDA Forest Service